RT Conference Proceedings
T1 Shadow Detection for Vehicle Detection in Urban Environments
A1 Hanif, Muhammad
A1 Hussain, Fawad
A1 Yousaf, Muhammad Haroon
A1 Velastin Carroza, Sergio Alejandro
A1 Chen, Zezhi
AB Finding an accurate and computationally efficient vehicle detection and classification algorithm for urban environment is challenging due to large video datasets and complexity of the task. Many algorithms have been proposed but there is no efficient algorithm due to various real-time issues. This paper proposes an algorithm which addresses shadow detection (which causes vehicles misdetection and misclassification) and incorporates solution of other challenges such as camera vibration, blurred image, illumination and weather changing effects. For accurate vehicles detection and classification, a combination of self-adaptive GMM and multi-dimensional Gaussian density transform has been used for modeling the distribution of color image data. RGB and HSV color space based shadow detection is proposed. Measurement-based feature and intensity based pyramid histogram of orientation gradient are used for classification into four main vehicle categories. The proposed method achieved 96.39% accuracy, while tested on Chile (MTT) dataset recorded at different times and weather conditions and hence suitable for urban traffic environment
PB Springer
SN 978-3-319-59875-8
YR 2017
FD 2017-07-02
LK https://hdl.handle.net/10016/28922
UL https://hdl.handle.net/10016/28922
LA eng
NO This paper has been presented at : 14th International Conference on Image Analysis and Recognition (ICIAR 2017)
DS e-Archivo
RD 18 jul. 2024